An Efficient Computational Method to Predict Drug-Target Interactions Utilizing Structural Perturbation Method

2020 ◽  
pp. 216-226
Author(s):  
Xinguo Lu ◽  
Fang Liu ◽  
Li Ding ◽  
Xinyu Wang ◽  
Jinxin Li ◽  
...  
Keyword(s):  
Perturbation Method ◽  
Drug Target ◽  
Computational Method ◽  
Structural Perturbation

scholarly journals Prediction of potential disease-associated microRNAs using structural perturbation method

2017 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Li Liu ◽  
Linyuan Lü ◽  
Quan Zou
Keyword(s):  
Perturbation Method ◽  
Experimental Studies ◽  
Computational Method ◽  
Identification Accuracy ◽  
Supplementary Information ◽  
Disease Network ◽  
Similarity Network ◽  
Structural Perturbation ◽  
Disease Associations ◽  
Reliable Performance

AbstractMotivationThe identification of disease-related microRNAs(miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA–disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases.ResultsThe link predictability of bilayer network is higher than that of miRNA–disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA–disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold cross-validation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA-disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predicted miRNAs have been manually confirmed by previous experimental studies.Availability and Implementationhttps://github.com/lecea/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals An efficient computational method for predicting drug-target interactions using weighted extreme learning machine and speed up robot features

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ji-Yong An ◽  
Fan-Rong Meng ◽  
Zi-Ji Yan
Keyword(s):  
Ion Channel ◽  
Extreme Learning Machine ◽  
Nuclear Receptor ◽  
Drug Target ◽  
Computational Method ◽  
Evolutionary Information ◽  
Support Vector ◽  
Weighted Extreme Learning Machine ◽  
Speed Up ◽  
Learning Machine

Abstract Background Prediction of novel Drug–Target interactions (DTIs) plays an important role in discovering new drug candidates and finding new proteins to target. In consideration of the time-consuming and expensive of experimental methods. Therefore, it is a challenging task that how to develop efficient computational approaches for the accurate predicting potential associations between drug and target. Results In the paper, we proposed a novel computational method called WELM-SURF based on drug fingerprints and protein evolutionary information for identifying DTIs. More specifically, for exploiting protein sequence feature, Position Specific Scoring Matrix (PSSM) is applied to capturing protein evolutionary information and Speed up robot features (SURF) is employed to extract sequence key feature from PSSM. For drug fingerprints, the chemical structure of molecular substructure fingerprints was used to represent drug as feature vector. Take account of the advantage that the Weighted Extreme Learning Machine (WELM) has short training time, good generalization ability, and most importantly ability to efficiently execute classification by optimizing the loss function of weight matrix. Therefore, the WELM classifier is used to carry out classification based on extracted features for predicting DTIs. The performance of the WELM-SURF model was evaluated by experimental validations on enzyme, ion channel, GPCRs and nuclear receptor datasets by using fivefold cross-validation test. The WELM-SURF obtained average accuracies of 93.54, 90.58, 85.43 and 77.45% on enzyme, ion channels, GPCRs and nuclear receptor dataset respectively. We also compared our performance with the Extreme Learning Machine (ELM), the state-of-the-art Support Vector Machine (SVM) on enzyme and ion channels dataset and other exiting methods on four datasets. By comparing with experimental results, the performance of WELM-SURF is significantly better than that of ELM, SVM and other previous methods in the domain. Conclusion The results demonstrated that the proposed WELM-SURF model is competent for predicting DTIs with high accuracy and robustness. It is anticipated that the WELM-SURF method is a useful computational tool to facilitate widely bioinformatics studies related to DTIs prediction.

scholarly journals Prediction of potential disease-associated microRNAs using structural perturbation method

2018 ◽  
Vol 34 (14) ◽  
pp. 2425-2432 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Li Liu ◽  
Linyuan Lü ◽  
Quan Zou
Keyword(s):  
Perturbation Method ◽  
Structural Perturbation

scholarly journals Link prediction of viral spike proteins and cell receptors using structural perturbation method

2021 ◽  
Author(s):  
Iris Zhou
Keyword(s):  
Perturbation Method ◽  
Amino Acid Sequences ◽  
New Host ◽  
Major Barrier ◽  
Similarity Network ◽  
Cell Receptors ◽  
Structural Perturbation ◽  
Protein Receptors ◽  
Human Viruses ◽  
Spike Proteins

Abstract Many protein receptors for animal and human viruses have been discovered in decades of studies. The main determinant of virus entry is the binding of the viral spike protein to host cell receptors, which mediates membrane fusion. In this work, a bilayer network is constructed by integrating the similarity network of the viral spike proteins, the similarity network of host receptors, and the association network between viruses and receptors. The structural perturbation method (SPM) is used to predict possible emerging infection of a virus in potential new host organisms. The reliability of this method is based on the hypothesis that the major barrier to virus infection is the differences in the compatibility of spike proteins and cell receptors, which is determined by the amino acid sequences among species.

scholarly journals DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

2021 ◽  
Author(s):  
Tilman Hinnerichs ◽  
Robert Hoehndorf
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Computational Method ◽  
Interaction Networks ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Link Type ◽  
Molecular Features

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.

scholarly journals SPRDA: a matrix completion approach based on the structural perturbation to infer disease-associated Piwi-Interacting RNAs

2020 ◽  
Author(s):  
Kai Zheng ◽  
Zhu-Hong You ◽  
Lei Wang ◽  
Leon Wong ◽  
Zhao-hui Zhan
Keyword(s):  
Perturbation Method ◽  
High Performance ◽  
Large Scale ◽  
Matrix Completion ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Biological Research ◽  
Sequence Information ◽  
Similarity Network ◽  
Structural Perturbation

AbstractEmerging evidence suggests that PIWI-interacting RNAs (piRNAs) are one of the most influential small non-coding RNAs (ncRNAs) that regulate RNA silencing. piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. The main motivation of this work is tantamount to fill the gap in research on large-scale prediction of disease-related piRNAs. In this study, a novel computational model based on the structural perturbation method is proposed, called SPRDA. In detail, the duplex network is constructed based on the piRNA similarity network and disease similarity network extracted from piRNA sequence information, Gaussian interaction profile kernel similarity information and gene-disease association information. The structural perturbation method is then used to predict the potential associations on the duplex network, which is more predictive than other network structures in terms of structural consistency. In the five-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

scholarly journals Link prediction of viral spike proteins and cell receptors using structural perturbation method

2021 ◽  
Author(s):  
Iris Zhou
Keyword(s):  
Perturbation Method ◽  
Amino Acid Sequences ◽  
New Host ◽  
Major Barrier ◽  
Similarity Network ◽  
Cell Receptors ◽  
Structural Perturbation ◽  
Protein Receptors ◽  
Human Viruses ◽  
Spike Proteins

Abstract Many protein receptors for animal and human viruses have been discovered in decades of studies. The main determinant of virus entry is the binding of the viral spike protein to host cell receptors, which mediates membrane fusion.In this work, a bilayer network is constructed by integrating the similarity network of the viral spike proteins, the similarity network of host receptors, and the association network between viruses and receptors. The structural perturbation method (SPM) is used to predict possible emerging infection of a virus in potential new host organisms. The reliability of this method is based on the hypothesis that the major barrier to virus infection is the differences in the compatibility of spike proteins and cell receptors, which is determined by the amino acid sequences among species.

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